Familial clustering of dysbiotic oral and fecal microbiomes in juvenile dermatomyositis

Juvenile dermatomyositis (JDM) is a rare immune-mediated disease of childhood with putative links to microbial exposures. In this multi-center, prospective, observational cohort study, we evaluated whether JDM is associated with discrete oral and gut microbiome signatures. We generated 16S rRNA sequencing data from fecal, saliva, supragingival, and subgingival plaque samples from JDM probands (n = 28). To control for genetic and environmental determinants of microbiome community structure, we also profiled microbiomes of unaffected family members (n = 27 siblings, n = 26 mothers, and n = 17 fathers). Sample type (oral-vs-fecal) and nuclear family unit were the predominant variables explaining variance in microbiome diversity, more so than having a diagnosis of JDM. The oral and gut microbiomes of JDM probands were more similar to their own unaffected siblings than they were to the microbiomes of other JDM probands. In a sibling-paired within-family analysis, several potentially immunomodulatory bacterial taxa were differentially abundant in the microbiomes of JDM probands compared to their unaffected siblings, including Faecalibacterium (gut) and Streptococcus (oral cavity). While microbiome features of JDM are often shared by unaffected family members, the loss or gain of specific fecal and oral bacteria may play a role in disease pathogenesis or be secondary to immune dysfunction in susceptible individuals.


Clinical characteristics of study participants
We enrolled 28 children with JDM (mean ± SEM 10.0 ± 0.7 years of age, 46% female) and unaffected family members as controls (27 healthy siblings, 26 mothers, and 17 fathers; Table 1) across two study sites (Seattle Children's Hospital [SCH], National Institutes of Health [NIH]).The JDM probands had mostly well-controlled disease activity with an average duration of disease of 4.25 ± 0.72 years; relevant clinical data are depicted in Table 1.Manual muscle test-8 (MMT-8) scores were available in 25 probands (89%) and ranged from 91 to 150.Based on these scores, 3 of 28 (11%) probands had abnormal muscle strength, most of which were in a range indicative of mild weakness (score greater than 135).Seven out of 28 probands had mild cutaneous disease activity (median score 2).Antinuclear antibodies (ANA) were recorded in 70% of JDM probands.Sixteen JDM probands were receiving one or more immunosuppressive medications, including methotrexate (n = 12), daily oral corticosteroids (n = 8), hydroxychloroquine (n = 7), intravenous immunoglobulin (IVIG) (n = 5), mycophenolate mofetil (n = 3), abatacept (n = 2), and/or tacrolimus (n = 1).Of the remaining probands, 10 were not receiving medical therapy for JDM, and 2 did not provide medication history.

Oral and fecal microbiomes clustered by family unit and particularly by sibling relationship
To identify key differences in microbiome diversity and community structure in JDM, we performed 16S rRNA amplicon sequencing of the V4 hypervariable region of oral and gut samples collected from these family-based cohorts (Tables S1, S2A).After filtering, we observed a total of 3,304 amplicon sequence variants (ASVs) throughout our dataset (Table S2B), of which 1,448 ASVs were detected in gut microbiomes, 1,928 ASVs were detected in oral microbiomes, and 72 ASVs were seen in subsets of both fecal and oral gut microbiomes (Fig. S1).Bacillota (formerly known as Firmicutes) and Bacteroidota (formerly known as Bacteroides) predominated in the fecal microbiomes (p < 10 -10 , two-tailed Student's t-test), whereas Pseudomonadota (formerly known as Proteobacteria), Fusobacteriota (formerly known as Fusobacteria), and Actinomycetota (formerly known as Actinobacteria) were also highly prevalent in the oral microbiomes (p < 10 -21 , two-tailed Student's t-test; Fig. 1A,B).Principal coordinates analysis (PCoA) based on weighted UniFrac distances demonstrated stark differences in oral and fecal microbiota community structures (Fig. 1C).Matching expectations of greater diversity and biomass in fecal than in oral samples, fecal microbiomes were characterized by greater alpha diversity than oral microbiomes (p < 10 -13 , two-tailed Student's t-test; Fig. S2A); similarly, genomic DNA concentrations were significantly higher in fecal than oral samples (p < 10 -11 , two-tailed Student's t-test; Fig. S2B).
We then calculated microbiome similarities within and between family units using UniFrac, a metric for quantifying pairwise phylogenetic distances between samples.Family unit was the predominant factor explaining variance in both weighted and unweighted UniFrac distances between samples for each sample type (p < 0.001 in fecal, saliva, supragingival swab, and posterior subgingival dental plaque samples; p < 0.01 in anterior subgingival dental plaque samples, permutational multivariate analysis of variance [PERMANOVA]).
Paired unweighted UniFrac distances between JDM probands and their own siblings were significantly smaller than the paired distances between JDM probands and their own parents (p < 0.05, two-tailed Student's t-test; Fig. 2A).In fact, the unweighted UniFrac distances between siblings was significantly smaller than the distances between JDM probands in our cohort (p < 0.002, two-tailed Student's t-test).This trend was also seen in a comparison of weighted UniFrac distances (p < 0.08, two-tailed Student's t-test; Fig. 2B).In other words, siblinghood was the most reliable of three indicators of fecal microbiome similarity in our study, which also included family unit and JDM diagnosis.autoantibodies assessed in the serum, 2 had no autoantibodies assessed, and the remainder had between 3 and 15 autoantibodies assessed.The numbers of JDM probands who had a given antibody checked are shown in brackets following each antibody name.The following antibodies are not included here because no JDM probands were positive for them: anti-Jo1, anti-PL-7, anti-PL-12, anti-EJ, anti-OJ, anti-KS, anti-SRP, anti-PM-Scl, and anti-Ku.***No medication data were available for 2 JDM probands; therefore, percentages reflect a denominator of 26.Total percentages do not add to 100% because several JDM probands were receiving multiple medications.Saliva, supragingival swab, and subgingival dental plaque microbiomes were highly similar between members of families (Fig. 2C-H).The UniFrac distances between siblings' oral microbiomes were not significantly different from the distances between JDM probands and their own parents (or between the healthy siblings and their own parents; two-tailed Student's t-tests; Fig. 2C-G).Intra-familial distances were significantly smaller than all inter-familial distances (p < 0.005, two-tailed Student's t-test; Fig. 2C-H).As with gut microbiomes, the UniFrac distances between oral microbiomes of JDM probands and their healthy siblings were significantly less than the distances between all JDM probands (p < 0.02, two-tailed Student's t-test), further supporting the notion of a stronger effect of sibling relationship than JDM diagnosis.UniFrac distances between oral microbiomes of parents within a household were significantly smaller than the distances between adults in different households (p < 0.05 for all comparisons, except weighted UniFrac distances of saliva samples, two-tailed Student's t-test; Fig. 2C-F)-a trend seen in fecal microbiomes as well (Fig. 2A).

Family units of JDM probands exhibit dysbiotic microbiomes
We next asked whether the familial clustering seen in our cohort of JDM probands and their nuclear families reflected familial dysbiosis.To compare gut microbiomes with healthy individuals lacking any known genetic or familial predisposition to JDM, we performed a cross-dataset analysis of adults 11,12 and children (selecting data from children ≥ 3 years of age) 13 whose gut microbiomes had been previously profiled by other groups using the same sequencing strategy and otherwise similar methods (including use of the same sample collection kit in one study 11 ; Table S2C).To mitigate potential biases imparted by batch effects, we employed Conditional Quantile Regression (ConQuR) to first remove microbiome batch effects 14 .Stool samples from individuals in our study (children with JDM, their unaffected siblings, and/or their parents) all clustered separately from samples from these three published datasets (p < 0.001 for both weighted and unweighted UniFrac distances, PERMANOVA; Fig. 3A,B).Acknowledging that there nonetheless remain differences in specific populations, protocols, and reagents that cannot be entirely controlled for, this may suggest that microbiomes of not only JDM probands, but entire JDM family units, are markedly different from previously reported healthy microbiomes.

Altered representation of immunomodulatory taxa in JDM probands
JDM was a significant factor explaining variance in weighted UniFrac distances in fecal and supragingival swab samples (p < 0.03, PERMANOVA).Therefore, we next sought to identify specific bacterial taxa differentiating children with JDM from their unaffected siblings in all fecal and oral samples, reasoning that differences in specific individual taxa may be biologically important.Our unique study design permitted us to adjust for microbiome biases attributable to family and age, e.g.via paired comparisons of JDM probands to their siblings.
In this section, we calculate significance without adjusting for multiple comparisons, a necessity based on small sample sizes given the rarity of JDM in the population.Thus, we refrain from reporting p-values or q-values, instead describing distributions of taxonomic abundances in different cohorts.

Discussion
Our study examined oral and fecal microbiomes in JDM, a rare pediatric multi-systemic autoimmune disease of unclear etiology.We studied JDM probands in comparison to their unaffected family members, employing healthy siblings as the best possible controls to match for both age and environmental influences.The fact that JDM patients and their siblings were found to have such similar microbiomes proved advantageous: controlling (to some extent) for microbiome effects related to shared genetic and environmental contexts (including genetic determinants of the microbiome community structure, vertically transmitted microbiomes, shared dietary and cultural influences, and co-habitation) permitted us to identify microbiome features with a greater likelihood of being clinically pertinent to JDM.Despite JDM probands in our study having very mild disease, we identified several bacteria that were significantly enriched or depleted in JDM, and each was observed in low abundance, consistent with findings of prior studies investigating the microbiome in immune-mediated diseases 15 .
We observed significant enrichment of Faecalibacterium and Ruminococcaceae (which are both members of the Lachnospiraceae family) in the fecal microbiomes of children with JDM.Faecalibacterium are typically associated with anti-inflammatory properties, and while adult studies elucidate their impact on inflammation 16,17 , their immune-modulating effects in children are unknown.A systematic review of fecal microbiomes in pediatric IBD reported a decrease in Faecalibacterium compared to healthy controls within a majority of the 41 included studies 18 .Intriguingly, Faecalibacterium are also reported to be are enriched in the gut microbiomes of children with Crohn's disease 19 .These mixed reports could be related to differences in the subclass of disease studied (ulcerative colitis vs Crohn's disease) or strain-to-strain variability in Faecalibacterium.Altered representation of Faecalibacterium has also been reported in Sjögren's syndrome 20 , RA 21 , and juvenile idiopathic arthritis (JIA) 22,23 .Fecal abundances of Ruminococcus species are greater in individuals with systemic lupus erythematosus, where it correlated with disease activity, and in patients with adult dermatomyositis (DM) 24 .These findings add to a body of evidence linking Lachnospiraceae bacterial species to immune-mediated diseases.
Additionally, we observed a significant enrichment of Roseburia and Muribaculaceae in the fecal microbiomes of children with JDM.Roseburia species have previously been associated with other autoimmune diseases such as type 1 diabetes 25 and have the potential to activate autoantibody responses in vivo 26 .Levels of Muribaculaceae have previously been shown to positively correlate with disease activity as well as pro-inflammatory cytokines IL-17, TNF-α, and IFN-γ in RA patients 27 .
Subdoligranulum, on the other hand, was a depleted taxon, consistent with a previous study that reported lower Subdoligranulum in Crohn's disease, suggesting it may be a putative probiotic in multiple contexts 28 .Indeed, Subdoligranulum variabile may be a potentially key member of the healthy human gut microbiome 29 .Furthermore, a recent study on juvenile idiopathic arthritis (JIA) in a small Swedish cohort indicated that Subdoligranulum abundances were significantly lower in the microbiomes of 1-year old children later diagnosed with JIA compared to controls, suggesting that microbiome differences may precede the onset of diagnosis 30 .
Interestingly, we observed significant decreases in 3 different Streptococcus sequences in the fecal samples of JDM probands, which contradicts previous reports of positive associations with autoimmune diseases, such as RA 21 , type 1 diabetes 25 , and Sjögren's syndrome 20 .This discrepancy could be due to differences in the disease nature of JDM, the age of affected individuals, use of immunosuppressive medications, or in the Streptococcus species themselves.Antibodies reactive to an epitope in S. pyogenes has been reported in patients with JDM and were found to be cross-reactive with a muscle-specific myosin protein M5 31 .Whether the antibodies contribute to depletion of Streptococcus in the gut or are unrelated cannot be determined from the data available.
Different bacteria comprised JDM signatures of oral samples; in fact, few ASVs were found in both oral and stool samples, consistent with recent studies suggesting that oral bacteria do not colonize the distal gut 32,33 .In the anterior subgingival dental plaque, two Fusobacterium ASVs were significantly enriched in JDM probands, consistent with previous studies reporting increased abundance in RA 34 and periodontal disease 35 , which has been linked to JDM 36 .Additionally, posterior subgingival dental plaque from JDM probands were enriched for S. pneumonia, which displays a plasma binding protein on its cell wall that may be immunogenic 37 .We also observed a significant enrichment of Neisseriaceae and Laurotrpia in JDM probands which have both been associated with RA 34 .Other notable enriched genera in JDM probands included Campylobacter and Gemella which have been associated with periodontal disease and JIA, respectively 38,39  www.nature.com/scientificreports/and periodontal disease 40,41 .JDM probands also had a significant reduction in Porphyromonas pasteri (the most abundant and prevalent Porphyromonas species in healthy adults 42,43 ) compared to their healthy siblings.Porphyromonas has been associated with RA in the subgingival plaque 34,40 .A recent report of Sjögren's syndrome found higher abundance of Porphyromonas pasteri in the oral microbiome of healthy controls 44 .Though our findings in the saliva and supragingival swab samples indicated that the microbial differences between JDM probands and their healthy siblings are among low abundant bacteria, we still observed interesting differences.For instance, JDM probands were enriched with Veillonella and Prevotella species in their saliva.A recent report of RA patients suggested that these microbes could predispose patients to the development of this autoimmune disease in the early stages 45 , consistent with our observations.Additionally, the increased abundance of a Neisseria species in the supragingival swab samples of JDM probands corroborates similar findings in the anterior subgingival dental plaque.The observation that species from the same genera (e.g.Streptococcus and Prevotella) were both enriched and depleted in JDM probands suggests that microbial-disease associations may be species-specific.Interestingly, each of these species has been correlative with autoimmune and periodontal diseases in other studies 34,46 .Thus, analyses using higher-level taxonomic classifications may lose the granularity needed to determine disease associations with specific bacterial species.Future studies involving cultivation and characterization of these, and other novel bacterial isolates derived from JDM patient samples, may provide insights into pathogenesis.
Our findings suggest that in depth microbial investigations of fecal and subgingival plaque samples (and perhaps less so salivary or supragingival swab samples) may be particularly revealing.We found that the nuclear family unit had a very strong impact on oral and gut microbiomes, as expected.In our cohort, children had lower alpha diversity than adults, consistent with prior reports demonstrating increasing alpha diversity throughout childhood until a plateau in early adulthood 13,47 .Unweighted UniFrac distances of fecal samples revealed JDM probands were more similar to their siblings than to their parents and other probands.These observations suggest that the sibling relationship has a larger effect on gut microbiome similarity than does carrying a diagnosis of JDM, and the similarities among siblings are most pronounced with respect to less abundant taxa.As the youngest child recruited was 3.5 years, this observation is not confounded by the rapidly evolving developmental microbiome program that occurs in the first 3 years of life 13,47 .Together, these findings offer further support to the generally accepted notion that environmental exposures play major roles in microbiome assembly and establishment in childhood.Overall, we observed smaller distances between family members living in the same household compared to unrelated individuals-evidencing convergence of microbiomes of unrelated individuals attributable to living in the same household, particularly with respect to rarer microbes 48 .Oral microbiomes of children within a household (i.e., JDM probands and their siblings) were no more similar than the microbiomes of adults in the same household.This finding may reflect effects of kissing, common meals and similar dietary habits, direct sharing of utensils, etc., among family members, although these specific aspects of family life were not queried in our study.
Our study has several limitations.First, we lacked control families without any immune-mediated diseases.We attempted to overcome this limitation by analyzing published datasets and found evidence of familial dysbiosis.However, based on the available data and our current analysis, we are unable to draw conclusions regarding immunological effects of these familial microbiome differences.One compelling hypothesis is that interactions between one's genetics and microbiome drive immune dysfunction a la the "ecological model of dysbiosis" 49 .While we were able to confirm that all NIH enrolled parents lived with their children, we were unable to confirm this for the SCH cohort.Given our small sample size, we were not able to do meaningful sub-analyses to demonstrate differences between known living with and living without a JDM proband.Second, our sample collection methods were designed solely for DNA-based profiling.Therefore, we could not culture bacterial isolates for direct testing of specific bacteria or bacterial metabolites in model systems of dysregulated immunity to study their functional impact.Third, our study was cross-sectional in design, a feature often inherent to studying a rare disease.Enrolled subjects largely had well-controlled or mildly active disease and were receiving immunosuppressive therapies, so we were unable to test for associations between microbial entities and disease activity.The study of Bae et al. did find a correlation of disease damage in patients with DM with lower microbial diversity 24 .
We were unable to distinguish potential disease-driving bacteria from those whose representation was altered because of microenvironmental changes.An ideal future study would be a longitudinal multicenter study where we could sample the microbiome of new-onset JDM patients just prior to initiation of therapy, at timepoints during therapy, and upon achieving remission to assess for changes in the microbiome.Fourth, due to limitations in sample size and medication use history, we were unable to assess or control for the effects of specific drugs and drug classes.It is known that immunosuppressive drugs such as methotrexate 50 , oral corticosteroids (i.e., prednisone) 51 , and mycophenolate mofetil 52 can alter the structure of bacterial communities.Despite these therapies, JDM probands and their siblings displayed high similarities in overall composition of oral and gut microbiomes.
Rare chronic pediatric diseases such as JDM are often challenging to diagnose and treat, and their diagnosis impacts entire family units.In this novel study of the oral and fecal microbiomes in JDM, in which we profiled JDM probands and their unaffected family members, we showed that family is a major influence on microbiome variability.This challenges the clinical paradigm of focusing on affected individuals and raises the prospect of whether practitioners ought to focus on family units in characterizing the microbiomes of patients.More broadly, our findings raise the question of whether microbiome-based therapies can ultimately succeed in treating individuals in isolation, or whether familial and social network units need to be addressed as a whole.In an analysis adjusting for microbiome differences attributable to family, we nevertheless found differences in several potentially immunomodulatory bacteria in patients with JDM.This study along with a recently-published study of the fecal microbiota in adult DM patients 24 provides an initial microbial landscape to contextualize future studies.Further research will be needed to understand the role of these bacteria in disease pathogenesis and whether targeting them with microbiome-based interventions may be of clinical therapeutic value.

Figure 1 .Figure 2 .
Figure 1.(A)Microbiota community structure at the phylum level of all samples sequenced in this study, separated by sample type.The 5 most prevalent phyla are represented.(B) Phylum-level differences of stool and oral samples.(C) PcoA plot based on weighted UniFrac distances of all samples in this study.The greatest separation is seen between fecal and oral samples along PC1, which accounts for the greatest variability throughout the dataset.Different oral sample types separate along PC2.

Figure 4 .
Figure 4. Differentially abundant ASVs in (A) fecal microbiomes, (B) anterior subgingival dental plaque samples, (C) and posterior subgingival dental plaque samples of JDM probands compared to unaffected siblings.As described in the text, post-hoc taxonomy was assigned based on BLAST results to the 4 ASVs designated by an asterisk (*) that were initially classified as unannotated bacteria.

Table 1 .
Summary of clinical and demographic characteristics of study participants.*Race and ethnicity were self-reported.Mixed race denotes combinations of Caucasian, Native American, Alaskan Native, Black/ African American, Native Hawaiian, and/or Pacific Islander.**Out of 28 JDM probands, 8 had all 19 myositis